Greedy path planning for maximizing value of information in underwater sensor networks

Underwater sensor networks (UWSNs) face specific challenges due to the transmission properties in the underwater environment. Radio waves propagate only for short distances under water, and acoustic transmissions have limited data rate and relatively high latency. One of the possible solutions to these challenges involves the use of autonomous underwater vehicles (AUVs) to visit and offload data from the individual sensor nodes. We consider an underwater sensor network visually monitoring an offshore oil platform for hazards such as oil spills from pipes and blowups. To each observation chunk (image or video) we attach a numerical value of information (VoI). This value monotonically decreases in time with a speeed which depends on the urgency of the captured data. An AUV visits different nodes along a specific path and collects data to be transmitted to the customer. Our objective is to develop path planners for the movement of the AUV which maximizes the total VoI collected. We consider three different path planners: the lawn mower path planner (LPP), the greedy planner (GPP) and the random planner (RPP). In a simulation study we compare the total VoI collected by these algorithms and show that the GPP outperforms the other two proposed algorithms on the studied scenarios

Value of Information Analysis in the Smart Agriculture Scenario using Wireless Internet of Things

openThe trend of Internet of Things development in the agriculture sector has led a high demand towards advanced technological settings with high efficiency and effectiveness. Often, the constructed sensor network suffers from excessive energy consumption due to the existence of collisions, and/or redundant data transmissions (time and space redundancy). In recent years, researchers have been trying to resolve this phenomenon by introducing a new quantitative metric, named Value of Information, which determines how valuable a generated information is. We want to make sure that the cost we spend for transmitting a packet corresponds to the value of the information that a packet submitted. In this thesis, we analyze such a metric from the agriculture point of view. Practical applications of this rationale include the reduction of update frequency by sensor considering the cost and network models that consider the transmissions of valuable packet only. These problems are evaluated through numerical simulation, in practical implementation contexts of a Lora network in real plantation and from a general perspective of future implementation.The trend of Internet of Things development in the agriculture sector has led a high demand towards advanced technological settings with high efficiency and effectiveness. Often, the constructed sensor network suffers from excessive energy consumption due to the existence of collisions, and/or redundant data transmissions (time and space redundancy). In recent years, researchers have been trying to resolve this phenomenon by introducing a new quantitative metric, named Value of Information, which determines how valuable a generated information is. We want to make sure that the cost we spend for transmitting a packet corresponds to the value of the information that a packet submitted. In this thesis, we analyze such a metric from the agriculture point of view. Practical applications of this rationale include the reduction of update frequency by sensor considering the cost and network models that consider the transmissions of valuable packet only. These problems are evaluated through numerical simulation, in practical implementation contexts of a Lora network in real plantation and from a general perspective of future implementation

Heuristics for a vehicle routing problem with information collection in wireless networks

International audienceWe consider a wireless network where a given set of stations is continuously generating information. A single vehicle, located at a base station, is available to collect the information via wireless transfer. The wireless transfer vehicle routing problem (WTVRP) is to decide which stations should be visited in the vehicle route, how long shall the vehicle stay in each station, and how much information shall be transferred from the nearby stations to the vehicle during each stay. The goal is to collect the maximum amount of information during a time period after which the vehicle returns to the base station. The WTVRP is NP-hard. Although it can be solved to optimality for small size instances, one needs to rely on good heuristic schemes to obtain good solutions for large size instances. In this work, we consider a mathematical formulation based on the vehicle visits. Several heuristics strategies are proposed, most of them based on the mathematical model. These strategies include constructive and improvement heuristics. Computational experiments show that a strategy that combines a combinatorial greedy heuristic to design a initial vehicle route, improved by a fix-and-optimize heuristic to provide a local optimum, followed by an exchange heuristic, affords good solutions within reasonable amount of running time

Maximizing The Value Of Sensed Information In Underwater Wireless Sensor Networks Via An Autonomous Underwater Vehicle

This paper considers underwater wireless sensor networks (UWSNs) for submarine surveillance and monitoring. Nodes produce data with an associated value, decaying in time. An autonomous underwater vehicle (AUV) is sent to retrieve information from the nodes, through optical communication, and periodically emerges to deliver the collected data to a sink, located on the surface or onshore. Our objective is to determine a collection path for the AUV so that the Value of Information (VoI) of the data delivered to the sink is maximized. To this purpose, we first define an Integer Linear Programming (ILP) model for path planning that considers realistic data communication rates, distances, and surfacing constraints. We then define the first heuristic for path finding that is adaptive to the occurrence of new events, relying only on acoustic communication for exchanging short control messages. Our Greedy and Adaptive AUV Path-finding (GAAP) heuristic drives the AUV to collect packets from nodes to maximize the VoI of the delivered data. We compare the VoI of data obtained by running the optimum solution derived by the ILP model to that obtained from running GAAP over UWSNs with realistic and desirable size. In our experiments GAAP consistently delivers more than 80% of the theoretical maximum VoI determined by the ILP model. © 2014 IEEE

Decision-making for Vehicle Path Planning

This dissertation presents novel algorithms for vehicle path planning in scenarios where the environment changes. In these dynamic scenarios the path of the vehicle needs to adapt to changes in the real world. In these scenarios, higher performance paths can be achieved if we are able to predict the future state of the world, by learning the way it evolves from historical data. We are relying on recent advances in the field of deep learning and reinforcement learning to learn appropriate world models and path planning behaviors. There are many different practical applications that map to this model. In this dissertation we propose algorithms for two applications that are very different in domain but share important formal similarities: the scheduling of taxi services in a large city and tracking wild animals with an unmanned aerial vehicle. The first application models a centralized taxi dispatch center in a big city. It is a multivariate optimization problem for taxi time scheduling and path planning. The first goal here is to balance the taxi service demand and supply ratio in the city. The second goal is to minimize passenger waiting time and taxi idle driving distance. We design different learning models that capture taxi demand and destination distribution patterns from historical taxi data. The predictions are evaluated with real-world taxi trip records. The predicted taxi demand and destination is used to build a taxi dispatch model. The taxi assignment and re-balance is optimized by solving a Mixed Integer Programming (MIP) problem. The second application concerns animal monitoring using an unmanned aerial vehicle (UAV) to search and track wild animals in a large geographic area. We propose two different path planing approaches for the UAV. The first one is based on the UAV controller solving Markov decision process (MDP). The second algorithms relies on the past recorded animal appearances. We designed a learning model that captures animal appearance patterns and predicts the distribution of future animal appearances. We compare the proposed path planning approaches with traditional methods and evaluated them in terms of collected value of information (VoI), message delay and percentage of events collected